Pathological waste generated in healthcare facilities poses a significant risk of infectious disease transmission and therefore requires specialized collection, transportation, and disposal systems. Efficient logistical planning is essential for companies responsible for managing this waste in order to reduce operational costs while ensuring adequate health and environmental standards. In this context, this study develops an optimization strategy based on the Periodic Vehicle Routing Problem (PVRP) to support the planning of pathological waste collection and transportation activities. A mixed-integer linear programming model (MILP) is proposed to determine the visit schedule for healthcare centers, assign vehicles to service days, and design vehicle routes that satisfy demand and service frequency requirements over a weekly planning horizon while minimizing total operational costs. The model is validated through a real case study from Argentina, demonstrating its applicability to real-world waste management systems. Additionally, a benchmark case from the literature is solved to evaluate the impact and performance of the proposed approach under a deterministic demand scenario derived from historical data. Finally, the performance of the proposed model is compared with alternative PVRP formulations using several benchmark instances, showing the reliability and consistency of the solutions obtained.

Presently, attaining sustainability in construction activities by integrating environmental, social, and financial viewpoints, is one of the most difficult objectives for firms, so adopting and implementing sustainable project management (SPM) is fundamental in countering this challenge. Therefore, this present study uses institutional theory to analyze how external factors such as isomorphic pressures contribute to the adoption and implementation of SPM in a developing country. Using a survey research strategy with self-administered questionnaires, data were obtained from 641 construction firms in Ghana. Partial Least Squares Structural Equation Modeling (PLS-SEM) approach was utilized to analyze the measurement and structural model. The outcomes suggest that isomorphic pressures are positively related to SPM with mimetic pressures asserting more impact and coercive pressures with the least influence. These findings offer construction professionals and policymakers important new perspectives that will help them to promote SPM so as to accomplish the Sustainable Development Goals.

The construction industry is acknowledged as one of the major primary energy consumers and contributors to global environmental emissions. To this end, proper selection of building materials is imperative to maintain the sustainability of a built environment. This research study proposed a building information modeling-based framework for lifecycle assessment of building materials in Saudi Arabia. Both Autodesk Revit and Autodesk Quantity Takeoff were adopted to define building materials, components, and their quantities. In addition, Athena Impact Estimator and Ecotect® Analysis software were leveraged to conduct thorough environmental impact assessment and energy simulation of building materials. The conducted lifecycle impact assessment tackled project phases of site preparation, construction, use, and demolition. The environmental dimensions of energy consumption, global warming, air emissions, land emissions, and water emissions were also explored. The capabilities of the developed model were tested using a case study of a college building in Saudi Arabia. The findings from this study can assist in the selection of environmentally friendly building materials that can be employed in Saudi Arabia’s construction market.

Mathematical modeling has become an important tool for understanding and controlling infectious diseases. It allows researchers to simulate and predict the spread of diseases, identify key transmission factors, assess the impact of interventions, and inform decision-making for disease control. Researchers can use mathematical models to explore different scenarios, evaluate the effectiveness of various interventions, and estimate the potential outcomes of different control strategies. This enables policymakers and public health professionals to make informed decisions and implement targeted measures to mitigate the impact of infectious diseases. In addition, mathematical models can be used to examine and evaluate the effectiveness of control strategies such as vaccination, social restrictions, and drug use. The results show that prevention strategies such as population vaccination and social restrictions can significantly help reduce the spread of influenza. This article presents a mathematical model for Influenza A (H1N1), as well as two other models specifically for Influenza A (H1N1) after quarantine and treatment. The purpose of the article is to provide a brief review of these models and compare them.

Air pollution remains a critical public health and environmental issue in megacities, with fine particulate matter (PM2.5) posing significant risks in Tehran, Iran. Traditional modeling methods often fail to capture the non-stationary and multiscale characteristics of urban air pollution dynamics. This study introduces a wavelet-based analytical framework to uncover both long-term seasonal trends and short-lived pollution events using a seven-year dataset (March 2017–March 2024) from nine monitoring stations across Tehran. We applied the Continuous Wavelet Transform (CWT) with Morlet and Daubechies 6 (db6) wavelets to decompose the PM2.5 time series into multiple temporal scales. The Morlet wavelet identified persistent winter peaks associated with temperature inversions and atmospheric stagnation, while db6 revealed abrupt, high-frequency pollution spikes linked to localized traffic emissions and industrial activity. Wavelet coherence analysis further illustrated the relationship between meteorological variables such as temperature, east-west winds, and north-south winds and PM2.5 concentrations, highlighting how these factors modulate air quality across different spatial and temporal scales. While ARIMA demonstrated stronger point-forecasting accuracy, the wavelet-based framework provided superior interpretability by preserving both gradual seasonal cycles and transient anomalies simultaneously structures that conventional models tend to smooth out. This comparison was conducted not to establish forecasting superiority, but to highlight the distinct and complementary role of wavelet analysis as a diagnostic tool for pollution hotspot identification and early-warning systems rather than pure numerical prediction. These findings underscore the value of wavelet analysis as not only an advanced analytical tool but also a decision-support system for real-time air quality monitoring and spatially targeted policy planning. By capturing the full spectrum of PM2.5 dynamics from chronic seasonal exposure to acute pollution spikes, this approach offers a more comprehensive framework for adaptive urban air quality management in complex environments like Tehran.